Computational Design of Novel Allosteric Inhibitors for Plasmodium falciparum DegP

The application of WaterMap-guided structure-based virtual screening in novel drug discovery

INTRODUCTION

Nowadays, it is widely accepted that water molecules play a key role in binding a ligand to a molecular target. Neglecting water molecules in the process of molecular recognition was the result of several failures of the structure-based drug discovery campaigns. The application of WaterMap, in particular WaterMap-guided molecular docking, enables the reasonably accurate and quick description of the location and energetics of water molecules at the ligand-protein interface.

AREAS COVERED

In this review, the authors shortly discuss the importance of water in drug design and discovery and provide a brief overview of the computational approaches used to predict the solvent-related effects for the purposes of presenting WaterMap in the context of other available techniques and tools. A concise description of WaterMap concept is followed by the presentation of WaterMap-assisted virtual screening literature published between 2013 and 2023.

EXPERT OPINION

In recent years, WaterMap software has been extensively used to support structure-based drug design, in particular structure-based virtual screening. Indeed, it is a useful tool to rescore docking results considering water molecules in the binding pocket. Although WaterMap allows for the consideration of the dynamic behavior of water molecules in the binding site, for best accuracy, its application in conjunction with other techniques such as molecular mechanics-generalized Born surface area of FEP (Free Energy Perturbation) is recommended.

Purification and Characterization of a DegP-Type Protease from the Marine Bacterium Cobetia amphilecti KMM 296

A new member of the DegP-type periplasmic serine endoproteases of the S1C family from the marine bacterium Cobetia amphilecti KMM 296 (CamSP) was expressed in Escherichia coli cells. The calculated molecular weight, number of amino acids, and isoelectric point (pI) of the mature protein CamSP are 69.957 kDa, 666, and 4.84, respectively. The proteolytic activity of the purified recombinant protease CamSP was 2369.4 and 1550.9 U/mg with the use of 1% bovine serum albumin (BSA) and casein as the substrates, respectively. The enzyme CamSP exhibited maximum activity at pH 6.0-6.2, while it was stable over a wide pH range from 5.8 to 8.5. The optimal temperature for the CamSP protease activity was 50 °C. The enzyme required NaCl or KCl at concentrations of 0.3 and 0.5 M, respectively, for its maximum activity. The Michaelis constant (K_m) and V_max for BSA were determined to be 41.7 µg/mL and 0.036 µg/mL min^-1, respectively. The metal ions Zn²⁺, Cu²⁺, Mn²⁺, Li²⁺, Mg²⁺, and Ca²⁺ slightly activated CamSP, while the addition of CoCl₂ to the incubation mixture resulted in a twofold increase in its protease activity. Ethanol, isopropanol, glycerol, and Triton-X-100 increased the activity of CamSP from two- to four-times. The protease CamSP effectively degraded the wheat flour proteins but had no proteolytic activity towards soybean, corn, and the synthetic substrates, α-benzoyl-Arg-p-nitroanilide (BAPNA) and N-Succinyl-L-alanyl-L-alanyl-L-prolyl-L-phenylalanine 4-nitroanilide (SAPNA).

Quantum Mechanical-Cluster Approach to Solve the Bioisosteric Replacement Problem in Drug Design

Bioisosteres are molecules that differ in substituents but still have very similar shapes. Bioisosteric replacements are ubiquitous in modern drug design, where they are used to alter metabolism, change bioavailability, or modify activity of the lead compound. Prediction of relative affinities of bioisosteres with computational methods is a long-standing task; however, the very shape closeness makes bioisosteric substitutions almost intractable for computational methods, which use standard force fields. Here, we design a quantum mechanical (QM)-cluster approach based on the GFN2-xTB semi-empirical quantum-chemical method and apply it to a set of H → F bioisosteric replacements. The proposed methodology enables advanced prediction of biological activity change upon bioisosteric substitution of -H with -F, with the standard deviation of 0.60 kcal/mol, surpassing the ChemPLP scoring function (0.83 kcal/mol), and making QM-based ΔΔG estimation comparable to ∼0.42 kcal/mol standard deviation of in vitro experiment. The speed of the method and lack of tunable parameters makes it affordable in current drug research.

Computational discovery of binding mode of anti-TRBC1 antibody and predicted key amino acids of TRBC1

Peripheral T-cell lymphoma (PTCL) is a type of non-Hodgkin lymphoma that progresses aggressively with poor survival rate. CAR T cell targeting T-cell receptor β-chain constant domains 1 (TRBC1) of malignant T cells has been developed recently by using JOVI.1 monoclonal antibody as a template. However, the mode of JOVI.1 binding is still unknown. This study aimed to investigate the molecular interaction between JOVI.1 antibody and TRBC1 by using computational methods and molecular docking. Therefore, the TRBC protein crystal structures (TRBC1 and TRBC2) as well as the sequences of JOVI.1 CDR were chosen as the starting materials. TRBC1 and TRBC2 epitopes were predicted, and molecular dynamic (MD) simulation was used to visualize the protein dynamic behavior. The structure of JOVI.1 antibody was also generated before the binding mode was predicted using molecular docking with an antibody mode. Epitope prediction suggested that the N3K4 region of TRBC1 may be a key to distinguish TRBC1 from TCBC2. MD simulation showed the major different surface conformation in this area between two TRBCs. The JOVI.1-TRBC1 structures with three binding modes demonstrated JOVI.1 interacted TRBC1 at N3K4 residues, with the predicted dissociation constant (K_d) ranging from 1.5 × 10⁸ to 1.1 × 10¹⁰ M. The analysis demonstrated JOVI.1 needed D1 residues of TRBC1 for the interaction formation to N3K4 in all binding modes. In conclusion, we proposed the three binding modes of the JOVI.1 antibody to TRBC1 with the new key residue (D1) necessary for N3K4 interaction. This data was useful for JOVI.1 redesign to improve the PTCL-targeting CAR T cell.